MLA-C01 Practice Test 41 — 15 Questions

Question 1

An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Add a Catch rule in the Step Functions state machine to invoke a Lambda alert function. Step Functions supports error handling via Catch rules; a Catch on the training or evaluation task can transition to a Lambda function that sends an alert.

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Use SQS dead-letter queue for failed training jobs

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Configure SageMaker training job to publish to SNS on failure

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Use EventBridge to monitor the training job status

Question 2

A company wants to ensure that only authorized users and services can invoke a SageMaker real-time endpoint. Which AWS service can be used to manage access control?

Accepted Answer

AWS Identity and Access Management (IAM). AWS Identity and Access Management (IAM) is the correct service because it allows you to create fine-grained permissions policies that control which users, roles, or services can invoke a SageMaker real-time endpoint via the InvokeEndpoint API. By attaching IAM policies to principals (e.g., IAM users, roles, or federated identities), you can restrict invocation based on conditions such as source IP, VPC endpoint, or MFA, ensuring only authorized entities can send inference requests.

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Amazon CloudWatch

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AWS CloudTrail

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AWS Config

Question 3

A company runs a batch inference job on 10 TB of image data stored in S3. Each image needs to be processed by a GPU-accelerated model. The job is not time-sensitive and cost is the primary concern. Which SageMaker option is MOST appropriate?

Accepted Answer

SageMaker Batch Transform with GPU instance and spot instances. Batch Transform with GPU spot instances is the most cost-effective choice for a non-time-sensitive, large-scale batch inference job on 10 TB of data. Spot instances offer up to 90% cost savings over on-demand, and Batch Transform natively handles splitting the dataset, distributing work across instances, and writing results to S3 without requiring a persistent endpoint.

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SageMaker Serverless Inference

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SageMaker Async Inference with GPU

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SageMaker real-time endpoint on GPU instances

Question 4

Refer to the exhibit. A data scientist configured an automatic model tuning job for a classification model. The tuning job completed after 20 training jobs, but the best validation accuracy was only 0.65. What is the most effective way to potentially improve the result?

Accepted Answer

Increase MaxNumberOfTrainingJobs to 100. Increasing MaxNumberOfTrainingJobs to 100 allows the automatic model tuning job to explore a larger hyperparameter space, giving the Bayesian optimization strategy (the default) more trials to converge on a better configuration. With only 20 training jobs, the tuner may not have had enough iterations to balance exploration and exploitation, especially for a complex classification model. More jobs increase the likelihood of finding a hyperparameter combination that yields higher validation accuracy.

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Change the strategy to Random

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Change the objective metric to training:accuracy

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Increase MaxParallelTrainingJobs to 10

Question 5

A social media company is processing a real-time stream of user activity data from Amazon Kinesis Data Streams to train a machine learning model for content recommendation. The raw data includes user ID, timestamp, content ID, interaction type (like, share, comment), and device type. The data scientists need to aggregate features per user over a sliding window of 7 days, including counts of interaction types, unique content IDs engaged, and a moving average of interaction timestamps. The aggregated data will be used to update a user embedding model. The streaming data volume is approximately 500 records per second, and the company uses an AWS Glue streaming ETL job for transformation. However, the Glue job is failing frequently with high latency and checkpoint errors. The team needs a more robust solution to prepare the streaming data features. Which approach should the team take?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.. Amazon Kinesis Data Analytics for Apache Flink provides native support for sliding window aggregations with managed state and exactly-once processing semantics, which directly addresses the high latency and checkpoint errors seen in the Glue streaming ETL job. Flink's checkpointing mechanism ensures fault-tolerant state management for the 7-day sliding window, while Glue's Spark Streaming engine struggles with long-running stateful operations at 500 records/sec due to its micro-batch architecture and checkpoint overhead.

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Increase the DPU count on the Glue streaming ETL job and reduce the checkpoint interval to improve performance.

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Use AWS Lambda functions to process records from Kinesis, store intermediate aggregation results in Amazon DynamoDB, and read them back to compute windowed features.

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Use Amazon SageMaker Processing jobs that run periodically every hour to read data from S3 (landing from Kinesis Firehose) and perform the aggregations batch-wise.

Question 6

A company wants to forecast monthly sales that show clear seasonality. Which algorithm is most suitable?

Accepted Answer

ARIMA (Seasonal ARIMA). Seasonal ARIMA (SARIMA) extends ARIMA by explicitly modeling seasonal components through seasonal differencing and seasonal autoregressive/moving average terms, making it the most suitable algorithm for forecasting monthly sales with clear seasonality. It captures both trend and seasonal patterns by incorporating parameters for the seasonal period (e.g., 12 for monthly data) and can handle non-stationary time series.

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Random forest

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K-means clustering

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Linear regression

Question 7

A company wants to use a pre-trained NLP model from SageMaker JumpStart for sentiment analysis. Which step is required to make predictions?

Accepted Answer

Deploy the model to an endpoint. D is correct because SageMaker JumpStart provides pre-trained models that are ready for inference without additional training. To make predictions, you must deploy the model to a SageMaker endpoint, which creates a hosted inference endpoint that can accept input data and return sentiment analysis results.

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Label the dataset for fine-tuning

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Train the model from scratch on the company's data

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Convert the model to ONNX format

Question 8

An engineer runs: aws sagemaker describe-endpoint --endpoint-name my-endpoint and receives the exhibit output. The engineer wants to update the endpoint to use a new model version stored in ECR with tag ':2'. Which step is necessary to perform the update?

Accepted Answer

Create a new endpoint configuration (my-endpoint-config-v2) referencing the new image, then call update-endpoint with the new config name.. SageMaker endpoints are immutable with respect to their configuration; you cannot modify an existing endpoint configuration in place. To update an endpoint to use a new model version, you must create a new endpoint configuration (e.g., my-endpoint-config-v2) that points to the new ECR image tag ':2', then call update-endpoint with the new configuration name. This triggers a zero-downtime deployment where SageMaker gradually shifts traffic to the new variant.

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Modify the existing endpoint configuration (my-endpoint-config-v1) to use the new image, then update the endpoint.

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Use the update-endpoint command directly with the new image ARN.

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Delete the endpoint and recreate it with the new model image.

Question 9

A company uses SageMaker Pipelines to automate model retraining. The pipeline runs daily but sometimes fails due to data quality issues. What is the best design to handle this?

Accepted Answer

Add a data quality check step with Conditional to skip training if data fails.. SageMaker Pipelines supports a data quality check step that can be integrated with a ConditionStep. If the data quality check fails, the ConditionStep can skip the training step entirely, preventing the pipeline from failing due to bad data. This design ensures the pipeline completes successfully (or exits gracefully) without wasting compute resources on training with invalid data.

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Use SageMaker Debugger to monitor training.

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Use SageMaker Model Registry to track model versions.

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Increase the instance size for the training step.

Question 10

A company deploys a model using SageMaker and enables data capture for monitoring. After a week, they notice that the captured data is not being written to the specified S3 bucket. The endpoint is running and invocations are successful. What is the most likely cause?

Accepted Answer

The IAM role used for the endpoint does not have s3:PutObject permission for the capture bucket.. The most likely cause is that the IAM role associated with the SageMaker endpoint lacks the `s3:PutObject` permission for the target S3 bucket. Without this permission, the endpoint cannot write the captured inference data to S3, even though invocations succeed because the model itself does not require S3 write access to serve predictions.

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The capture bucket is in a different region.

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The endpoint is using a multi-model endpoint which does not support data capture.

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The DataCaptureConfig parameter in the endpoint configuration is missing the "CaptureOptions" field.

Question 11

A team is building a recommendation system and wants to store and serve features for online and offline models. The features include user statistics (updated daily) and movie metadata (static). The team needs low-latency inference for real-time recommendations and wants to reuse features across multiple models. Which AWS service should the team use to store, manage, and serve these features?

Accepted Answer

SageMaker Feature Store.. Amazon SageMaker Feature Store is purpose-built for storing, managing, and serving ML features with low-latency retrieval for online inference and batch serving for offline training. It supports feature reuse across multiple models by providing a centralized feature registry, consistent feature definitions, and both online (low-latency) and offline (S3-based) stores, which directly matches the team's requirements for real-time recommendations and cross-model reuse.

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Amazon DynamoDB with TTL.

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AWS Glue Data Catalog.

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Amazon S3 with AWS Lambda for serving.

Question 12

A team is deploying a TensorFlow model on a SageMaker real-time endpoint with automatic scaling. They set the scaling policy to target an average CPU utilization of 50%. However, during traffic spikes, the endpoint experiences high latency and 503 errors. The instance type is ml.c5.large. What should the team do to resolve this while minimizing cost?

Accepted Answer

Add a scaling policy based on the number of concurrent requests per instance. Scaling based on CPU utilization alone is often insufficient for inference workloads where latency is the primary concern. By adding a scaling policy based on the number of concurrent requests per instance, the team can proactively scale out before CPU saturation occurs, reducing latency and eliminating 503 errors. SageMaker's automatic scaling supports multiple target tracking metrics, and using concurrent requests per instance aligns more closely with the actual demand on the model serving container.

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Pre-warm the endpoint by keeping a fixed number of additional instances

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Increase the scale-in cooldown period to avoid frequent downsizing

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Change the instance type to a larger one like ml.c5.xlarge to handle the spikes

Question 13

A data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?

Accepted Answer

Handle missing values (with median strategy). Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.

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Replace missing with constant

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Custom transform with Python

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Drop missing rows

Question 14

A company wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?

Accepted Answer

Amazon SageMaker Pipelines. Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.

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AWS CodePipeline

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AWS Glue Workflows

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AWS Step Functions

Question 15

A team is using Amazon SageMaker Processing for data preprocessing. They have a Parquet dataset in Amazon S3. Which configuration will provide the most efficient reading of the dataset during processing?

Accepted Answer

Read the Parquet files directly using SparkSession.read.parquet. SageMaker Processing natively integrates with Apache Spark, and reading Parquet files directly via `SparkSession.read.parquet` leverages columnar storage, predicate pushdown, and compression (e.g., Snappy) to minimize I/O and deserialization overhead. This approach is far more efficient than text-based or format-conversion methods, as Parquet is optimized for analytical workloads and preserves schema information.

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Read the Parquet files as text using SparkContext.textFile

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Split the dataset into many small Parquet files (e.g., 1 MB each)

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Convert the Parquet files to CSV before processing